Data Visualization Techniques Demo¶

This notebook demonstrates a range of data visualization techniques using Matplotlib, Seaborn, and Plotly. Datasets used: iris, tips, and flights from seaborn.

Setup: installs and imports¶

Run the next cell to import libraries. If Plotly is not installed, the cell will install it programmatically.

In [1]:
# Core imports and plotting defaults
import sys
import subprocess
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns

# Set styles
%matplotlib inline
sns.set_theme(style="darkgrid", context="notebook")
plt.rcParams['figure.figsize'] = (10, 5)
plt.rcParams['font.size'] = 12

# Ensure plotly is available
try:
    import plotly.express as px
except Exception:
    subprocess.check_call([sys.executable, '-m', 'pip', 'install', 'plotly'])
    import plotly.express as px

print('libraries loaded')

libraries loaded

Load example datasets¶

We'll load iris, tips, and flights from seaborn and inspect them briefly.

In [2]:
import seaborn as sns
iris = sns.load_dataset('iris')
tips = sns.load_dataset('tips')
flights = sns.load_dataset('flights')

print('iris:', iris.shape)
display(iris.head())
print('tips:', tips.shape)
display(tips.head())
print('flights:', flights.shape)
display(flights.head())

iris: (150, 5)
sepal_length sepal_width petal_length petal_width species
0 5.1 3.5 1.4 0.2 setosa
1 4.9 3.0 1.4 0.2 setosa
2 4.7 3.2 1.3 0.2 setosa
3 4.6 3.1 1.5 0.2 setosa
4 5.0 3.6 1.4 0.2 setosa 
tips: (244, 7)
total_bill tip sex smoker day time size
0 16.99 1.01 Female No Sun Dinner 2
1 10.34 1.66 Male No Sun Dinner 3
2 21.01 3.50 Male No Sun Dinner 3
3 23.68 3.31 Male No Sun Dinner 2
4 24.59 3.61 Female No Sun Dinner 4 
flights: (144, 3)
year month passengers
0 1949 Jan 112
1 1949 Feb 118
2 1949 Mar 132
3 1949 Apr 129
4 1949 May 121

Line plots & time series (Matplotlib / pandas)¶

Aggregate the flights dataset into a time series and demonstrate line plotting with rolling mean and shaded confidence band.

In [3]:
# prepare a datetime index from flights dataset
fl = flights.copy()
fl['month_num'] = pd.to_datetime(fl['month'], format='%b').dt.month
fl['date'] = pd.to_datetime(fl['year'].astype(str) + '-' + fl['month_num'].astype(str).str.zfill(2) + '-01')
ts = fl.set_index('date')['passengers'].sort_index()
ts = ts.asfreq('MS')

# plot with rolling mean and confidence band
fig, ax = plt.subplots()
ax.plot(ts.index, ts.values, label='passengers', color='C0') # type: ignore
rolling = ts.rolling(window=12).mean()
ax.plot(rolling.index, rolling.values, label='12-month rolling mean', color='C1')
# simple band: rolling +/- std
rolling_std = ts.rolling(window=12).std()
ax.fill_between(ts.index, (rolling - rolling_std).values, (rolling + rolling_std).values, color='C1', alpha=0.2)
ax.set_title('Monthly airline passengers (flights)')
ax.set_ylabel('passengers')
ax.legend()
Out[3]:
<matplotlib.legend.Legend at 0x7f802ce38fb0>
No description has been provided for this image

Scatter plots and regression overlays¶

Scatter plot for iris (sepal length vs sepal width) with Seaborn regression overlay.

In [4]:
# scatter with seaborn and regression line
fig, ax = plt.subplots()
sns.scatterplot(data=iris, x='sepal_length', y='sepal_width', hue='species', style='species', ax=ax, s=60)
sns.regplot(data=iris, x='sepal_length', y='sepal_width', scatter=False, ax=ax, color='gray', lowess=True)
ax.set_title('Iris: sepal length vs sepal width (with LOWESS)')
ax.legend(title='species')
Out[4]:
<matplotlib.legend.Legend at 0x7f802cd09df0>
No description has been provided for this image

Histograms and KDE¶

Histogram and KDE of tips.total_bill comparing density and bins.

In [5]:
fig, ax = plt.subplots()
sns.histplot(tips['total_bill'], bins=30, kde=True, stat='density', ax=ax, color='C2')
ax.set_title('Distribution of total bill (tips dataset)')
Out[5]:
Text(0.5, 1.0, 'Distribution of total bill (tips dataset)')
No description has been provided for this image

Boxplots and violin plots¶

Boxplot of total bill by day and violinplot with swarm overlay.

In [6]:
fig, axes = plt.subplots(ncols=2, figsize=(12,5))
# Use `hue`=x and remove the legend to avoid the palette-without-hue deprecation warning
sns.boxplot(data=tips, x='day', y='total_bill', hue='day', dodge=False, ax=axes[0], palette='pastel')
if axes[0].get_legend() is not None:
    axes[0].get_legend().remove()
axes[0].set_title('Boxplot: total_bill by day')
# For violinplot we also pass hue and then remove the generated legend
sns.violinplot(data=tips, x='day', y='total_bill', hue='day', split=False, inner=None, ax=axes[1], palette='muted')
if axes[1].get_legend() is not None:
    axes[1].get_legend().remove()
# overlay the raw points (swarm) as before
sns.swarmplot(data=tips, x='day', y='total_bill', color='k', size=3, ax=axes[1])
axes[1].set_title('Violin + swarm: total_bill by day')
Out[6]:
Text(0.5, 1.0, 'Violin + swarm: total_bill by day')
No description has been provided for this image

Heatmap and correlation matrix¶

Show correlations for the iris dataset.

In [7]:
corr = iris.select_dtypes(include=np.number).corr()
fig, ax = plt.subplots(figsize=(8,6))
sns.heatmap(corr, annot=True, cmap='coolwarm', center=0, ax=ax)
ax.set_title('Correlation matrix (iris)')
Out[7]:
Text(0.5, 1.0, 'Correlation matrix (iris)')
No description has been provided for this image

Pairplot for multivariate exploration¶

Pairwise relationships with hue by species. This can be slow on large datasets.

In [8]:
# Pairplot (can take a moment)
sns.pairplot(iris, hue='species', diag_kind='kde', corner=False)
Out[8]:
<seaborn.axisgrid.PairGrid at 0x7f80291d0260>
No description has been provided for this image

Interactive plots with Plotly Express¶

Interactive scatter (tips) and an interactive time series (flights).

In [9]:
import plotly.express as px
from IPython.display import HTML, display
# Create interactive scatter and attempt to show inline; fall back to HTML file if notebook renderer is missing
fig = px.scatter(tips, x='total_bill', y='tip', color='day', hover_data=['sex','size'], title='Tips: total bill vs tip')
try:
    fig.show()
except ValueError as _err:
    fname = 'interactive_tips.html'
    fig.write_html(fname, include_plotlyjs='cdn')
    display(HTML(f'<a href="{fname}" target="_blank">Open interactive_tips.html</a>'))

# interactive time series
ts_df = ts.reset_index().rename(columns={'index':'date', 'passengers':'passengers'})
fig2 = px.line(ts_df, x='date', y='passengers', title='Monthly passengers (interactive)')
try:
    fig2.show()
except ValueError:
    fname2 = 'interactive_flights.html'
    fig2.write_html(fname2, include_plotlyjs='cdn')
    display(HTML(f'<a href="{fname2}" target="_blank">Open interactive_flights.html</a>'))

Saving figures and exporting¶

Examples of saving static Matplotlib figures and exporting Plotly to HTML.

In [10]:
# save last matplotlib figure (if any)
fig_static, ax = plt.subplots()
ax.plot([0,1,2], [10,20,15])
ax.set_title('Example save')
fig_static.savefig('example_plot.png', dpi=150, bbox_inches='tight')
print('saved example_plot.png')

# save plotly to HTML
fig.write_html('interactive_tips.html')
print('saved interactive_tips.html')

saved example_plot.png
saved interactive_tips.html
No description has been provided for this image

Notes on performance and large datasets¶

For very large point clouds consider hexbin, datashader, or downsampling. plt.hexbin and sns.kde are useful for dense plots.

Datashader example (for very large datasets)¶

This cell demonstrates how to use datashader to render large scatter clouds efficiently. If datashader is not installed in your environment the cell will fall back to a Matplotlib hexbin plot and print installation instructions.

In [11]:
# Datashader example: create a large synthetic point cloud by repeating `tips` and adding jitter
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt

# build a larger dataset by repeating tips several times (adjust multiplier as needed)
mult = 2000  # result ~244 * 2000 ~ 488k rows; reduce if memory is constrained
try:
    big = pd.concat([tips]*mult, ignore_index=True)
except Exception:
    big = tips.sample(frac=1, replace=True).reset_index(drop=True)
# add small jitter so points are not identical
np.random.seed(1)
big['x'] = big['total_bill'] + np.random.normal(0, 0.1, size=len(big))
big['y'] = big['tip'] + np.random.normal(0, 0.05, size=len(big))

# Try to use datashader, otherwise fallback to hexbin
try:
    import datashader as ds
    import datashader.transfer_functions as tf
    import colorcet
    from IPython.display import HTML, display
    # create canvas and aggregate
    cvs = ds.Canvas(plot_width=800, plot_height=400)
    agg = cvs.points(big, 'x', 'y', ds.count())
    img = tf.shade(agg, cmap=colorcet.m_fire, how='eq_hist')
    display(img)
except Exception as e:
    print('datashader not available or failed to render (', e, '). Falling back to matplotlib hexbin.')
    fig, ax = plt.subplots(figsize=(10,4))
    hb = ax.hexbin(big['x'], big['y'], gridsize=80, cmap='viridis')
    fig.colorbar(hb, ax=ax, label='counts')
    ax.set_xlabel('total_bill (jittered)')
    ax.set_ylabel('tip (jittered)')
    ax.set_title('Hexbin fallback (datashader not installed)')
    plt.show()
    print('To enable datashader: pip install datashader colorcet')
No description has been provided for this image

Animated plots (Matplotlib FuncAnimation)¶

This example creates a simple animated sine wave using Matplotlib's FuncAnimation and displays it inline using HTML/JS. If your environment does not support JS rendering, you can save the animation to MP4/GIF (may require ffmpeg).

In [12]:
import numpy as np
import matplotlib.pyplot as plt
from matplotlib import animation
from IPython.display import HTML

fig, ax = plt.subplots()
x = np.linspace(0, 2*np.pi, 400)
line, = ax.plot(x, np.sin(x))
ax.set_ylim(-1.5, 1.5)
ax.set_title('Animated sine wave')

def init():
    line.set_ydata(np.sin(x))
    return (line,)

def animate(i):
    line.set_ydata(np.sin(x + i/10.0))
    return (line,)

anim = animation.FuncAnimation(fig, animate, init_func=init, frames=200, interval=30, blit=True)
plt.close(fig)
# Display as JS animation in the notebook (will embed JS/CSS)
HTML(anim.to_jshtml())
Out[12]:
No description has been provided for this image
In [13]:
# Export the previously-created `anim` to MP4 or GIF.
# This tries MP4 using ffmpeg first, then falls back to GIF via pillow.
from matplotlib import animation as anim_module
out_mp4 = 'sine_animation.mp4'
out_gif = 'sine_animation.gif'
saved = False
# Try MP4 (requires ffmpeg available on PATH)
try:
    Writer = anim_module.writers['ffmpeg']
    writer = Writer(fps=30, metadata=dict(artist='auto'), bitrate=1800)
    anim.save(out_mp4, writer=writer)
    print(f'Saved animation as MP4: {out_mp4}')
    saved = True
except Exception as e:
    print('MP4 export failed or ffmpeg not available:', e)
# Fallback to GIF using pillow writer
if not saved:
    try:
        anim.save(out_gif, writer='pillow', fps=30)
        print(f'Saved animation as GIF: {out_gif}')
        saved = True
    except Exception as e:
        print('GIF export failed:', e)
# Final message with install hints if nothing worked
if not saved:
    print('Unable to save animation. To enable MP4 export install ffmpeg on your system (e.g. apt install ffmpeg) and ensure it is on PATH.')
    print('For GIF export ensure pillow is installed: pip install pillow')

Saved animation as MP4: sine_animation.mp4

Requirements and how to run¶

Required Python packages: numpy, pandas, matplotlib, seaborn, plotly, statsmodels. Install with: pip install numpy pandas matplotlib seaborn plotly statsmodels

Recommended quick steps to run locally:

  1. Create and activate a virtual environment (recommended): python -m venv .venv On Linux/macOS: source .venv/bin/activate On Windows (PowerShell): ./.venv/Scripts/Activate.ps1
  2. Install dependencies: pip install -r requirements.txt or the pip line above.
  3. Start Jupyter: jupyter lab or jupyter notebook and open this file: /home/sysadmin/Notebooks/data-visualization-demo.ipynb.

Notes:

  • Plotly inline rendering may raise an error if nbformat or notebook MIME renderers are missing; the notebook already falls back to writing interactive HTML files and showing links.
  • To export animations to MP4/GIF you may need ffmpeg installed on your system.
  • For very large datasets consider using datashader (optional) or downsampling before plotting.

Conclusion and next steps¶

This notebook provided quick examples across Matplotlib, Seaborn, and Plotly. Next steps: experiment with custom palettes, GridSpec layouts, animations, or datashader for very large datasets.

To run locally: in a terminal run jupyter lab or jupyter notebook and open this file: /home/sysadmin/Notebooks/data-visualization-demo.ipynb.

Exporting this notebook to PDF¶

This section demonstrates how to produce a PDF copy of this notebook programmatically using nbconvert. The cell below first attempts a direct PDF export (which commonly requires a LaTeX installation such as TeX Live). If that fails it falls back to exporting an HTML file which you can open in a browser and print/save as PDF.

Note: the code assumes this notebook file is named data-visualization-demo.ipynb and is in the current working directory. If your notebook has a different name or path, update the nb variable in the code cell.

In [14]:
# Programmatic notebook -> PDF example with fallback to HTML
from pathlib import Path
import subprocess
import sys
import shutil

# Assumption: notebook file is in the current working directory with this name
nb = Path('data-visualization-demo.ipynb')
if not nb.exists():
    print(f'Notebook file not found at {nb.resolve()}. Update the `nb` variable to the correct path.')
else:
    out_pdf = nb.with_suffix('.pdf')

    def run_cmd(cmd):
        print('Running:', ' '.join(cmd))
        res = subprocess.run(cmd, stdout=subprocess.PIPE, stderr=subprocess.STDOUT, text=True)
        print(res.stdout)
        return res.returncode == 0

    success = False
    # Try jupyter nbconvert (preferred). Note: PDF conversion usually requires LaTeX on the system.
    jupyter = shutil.which('jupyter')
    if jupyter:
        success = run_cmd([jupyter, 'nbconvert', '--to', 'pdf', str(nb)])

    # If direct PDF failed, export to HTML and instruct the user to print to PDF from the browser
    if not success:
        print('Direct PDF export failed or is not available. Falling back to HTML export.')
        html_file = nb.with_suffix('.html')
        # Try nbconvert to HTML (more widely supported)
        if jupyter:
            ok = run_cmd([jupyter, 'nbconvert', '--to', 'html', str(nb)])
        else:
            ok = run_cmd([sys.executable, '-m', 'nbconvert', '--to', 'html', str(nb)])
        if ok:
            print(f'HTML exported to {html_file}. You can open it in a browser and use Print -> Save as PDF (or a headless browser) to create a PDF.')
        else:
            print('HTML export also failed. You may need to install jupyter/nbconvert: pip install jupyter nbconvert')

    else:
        print(f'PDF exported to: {out_pdf.resolve()}')

Running: /home/sysadmin/Projects/data-visualization-demo/.venv/bin/jupyter nbconvert --to pdf data-visualization-demo.ipynb
[NbConvertApp] Converting notebook data-visualization-demo.ipynb to pdf
[NbConvertApp] ERROR | Error while converting 'data-visualization-demo.ipynb'
Traceback (most recent call last):
  File "/home/sysadmin/Projects/data-visualization-demo/.venv/lib/python3.12/site-packages/nbconvert/nbconvertapp.py", line 487, in export_single_notebook
    output, resources = self.exporter.from_filename(
                        ^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/home/sysadmin/Projects/data-visualization-demo/.venv/lib/python3.12/site-packages/nbconvert/exporters/templateexporter.py", line 390, in from_filename
    return super().from_filename(filename, resources, **kw)  # type:ignore[return-value]
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/home/sysadmin/Projects/data-visualization-demo/.venv/lib/python3.12/site-packages/nbconvert/exporters/exporter.py", line 201, in from_filename
    return self.from_file(f, resources=resources, **kw)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/home/sysadmin/Projects/data-visualization-demo/.venv/lib/python3.12/site-packages/nbconvert/exporters/templateexporter.py", line 396, in from_file
    return super().from_file(file_stream, resources, **kw)  # type:ignore[return-value]
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/home/sysadmin/Projects/data-visualization-demo/.venv/lib/python3.12/site-packages/nbconvert/exporters/exporter.py", line 220, in from_file
    return self.from_notebook_node(
           ^^^^^^^^^^^^^^^^^^^^^^^^
  File "/home/sysadmin/Projects/data-visualization-demo/.venv/lib/python3.12/site-packages/nbconvert/exporters/pdf.py", line 184, in from_notebook_node
    latex, resources = super().from_notebook_node(nb, resources=resources, **kw)
                       ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/home/sysadmin/Projects/data-visualization-demo/.venv/lib/python3.12/site-packages/nbconvert/exporters/latex.py", line 92, in from_notebook_node
    return super().from_notebook_node(nb, resources, **kw)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/home/sysadmin/Projects/data-visualization-demo/.venv/lib/python3.12/site-packages/nbconvert/exporters/templateexporter.py", line 429, in from_notebook_node
    output = self.template.render(nb=nb_copy, resources=resources)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/home/sysadmin/Projects/data-visualization-demo/.venv/lib/python3.12/site-packages/jinja2/environment.py", line 1295, in render
    self.environment.handle_exception()
  File "/home/sysadmin/Projects/data-visualization-demo/.venv/lib/python3.12/site-packages/jinja2/environment.py", line 942, in handle_exception
    raise rewrite_traceback_stack(source=source)
  File "/home/sysadmin/Projects/data-visualization-demo/.venv/share/jupyter/nbconvert/templates/latex/index.tex.j2", line 8, in top-level template code
    ((* extends cell_style *))
    ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/home/sysadmin/Projects/data-visualization-demo/.venv/share/jupyter/nbconvert/templates/latex/style_jupyter.tex.j2", line 176, in top-level template code
    \prompt{(((prompt)))}{(((prompt_color)))}{(((execution_count)))}{(((extra_space)))}
    ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/home/sysadmin/Projects/data-visualization-demo/.venv/share/jupyter/nbconvert/templates/latex/base.tex.j2", line 7, in top-level template code
    ((*- extends 'document_contents.tex.j2' -*))
    ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/home/sysadmin/Projects/data-visualization-demo/.venv/share/jupyter/nbconvert/templates/latex/document_contents.tex.j2", line 51, in top-level template code
    ((*- block figure scoped -*))
    ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/home/sysadmin/Projects/data-visualization-demo/.venv/share/jupyter/nbconvert/templates/latex/display_priority.j2", line 5, in top-level template code
    ((*- extends 'null.j2' -*))
    ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/home/sysadmin/Projects/data-visualization-demo/.venv/share/jupyter/nbconvert/templates/latex/null.j2", line 30, in top-level template code
    ((*- block body -*))
  File "/home/sysadmin/Projects/data-visualization-demo/.venv/share/jupyter/nbconvert/templates/latex/base.tex.j2", line 241, in block 'body'
    ((( super() )))
  File "/home/sysadmin/Projects/data-visualization-demo/.venv/share/jupyter/nbconvert/templates/latex/null.j2", line 32, in block 'body'
    ((*- block any_cell scoped -*))
    ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/home/sysadmin/Projects/data-visualization-demo/.venv/share/jupyter/nbconvert/templates/latex/null.j2", line 85, in block 'any_cell'
    ((*- block markdowncell scoped-*)) ((*- endblock markdowncell -*))
    ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/home/sysadmin/Projects/data-visualization-demo/.venv/share/jupyter/nbconvert/templates/latex/document_contents.tex.j2", line 68, in block 'markdowncell'
    ((( cell.source | citation2latex | strip_files_prefix | convert_pandoc('markdown+tex_math_double_backslash', 'json',extra_args=[]) | resolve_references | convert_explicitly_relative_paths | convert_pandoc('json','latex'))))
    ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/home/sysadmin/Projects/data-visualization-demo/.venv/lib/python3.12/site-packages/nbconvert/filters/pandoc.py", line 36, in convert_pandoc
    return pandoc(source, from_format, to_format, extra_args=extra_args)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/home/sysadmin/Projects/data-visualization-demo/.venv/lib/python3.12/site-packages/nbconvert/utils/pandoc.py", line 50, in pandoc
    check_pandoc_version()
  File "/home/sysadmin/Projects/data-visualization-demo/.venv/lib/python3.12/site-packages/nbconvert/utils/pandoc.py", line 98, in check_pandoc_version
    v = get_pandoc_version()
        ^^^^^^^^^^^^^^^^^^^^
  File "/home/sysadmin/Projects/data-visualization-demo/.venv/lib/python3.12/site-packages/nbconvert/utils/pandoc.py", line 75, in get_pandoc_version
    raise PandocMissing()
nbconvert.utils.pandoc.PandocMissing: Pandoc wasn't found.
Please check that pandoc is installed:
https://pandoc.org/installing.html

Direct PDF export failed or is not available. Falling back to HTML export.
Running: /home/sysadmin/Projects/data-visualization-demo/.venv/bin/jupyter nbconvert --to html data-visualization-demo.ipynb
[NbConvertApp] Converting notebook data-visualization-demo.ipynb to html
/home/sysadmin/Projects/data-visualization-demo/.venv/share/jupyter/nbconvert/templates/base/display_priority.j2:32: UserWarning: Your element with mimetype(s) dict_keys(['application/vnd.plotly.v1+json']) is not able to be represented.
  {%- elif type == 'text/vnd.mermaid' -%}
[NbConvertApp] WARNING | Alternative text is missing on 9 image(s).
[NbConvertApp] Writing 8807583 bytes to data-visualization-demo.html

HTML exported to data-visualization-demo.html. You can open it in a browser and use Print -> Save as PDF (or a headless browser) to create a PDF.

Notes and alternatives¶

  • Direct PDF export via nbconvert --to pdf typically uses a LaTeX intermediate; installing a TeX distribution (TeX Live on Linux) is often required: e.g., sudo apt install texlive-xetex texlive-fonts-recommended texlive-latex-recommended.
  • If you prefer not to install LaTeX, export to HTML (nbconvert --to html) and then: open in a browser and Print -> Save as PDF, or use a headless browser (Chromium/Chrome) to print-to-pdf programmatically (e.g., Puppeteer/Playwright).
  • Example command-line alternative (if you have chromium): chromium --headless --disable-gpu --print-to-pdf=output.pdf data-visualization-demo.html
  • If you want, I can also add an automated Chromium-based HTML->PDF step (requires installing Chromium) or a Playwright/Pyppeteer example—tell me which fallback you'd prefer.

Automated Chromium HTML -> PDF (optional)¶

The cell below will look for a Chromium/Chrome binary on your PATH and, if found, will run it in headless mode to convert the previously-exported HTML (data-visualization-demo.html) to a PDF. This is useful when LaTeX is not installed and you want a fully automated HTML->PDF step.

If you don't have Chromium/Chrome installed, the cell will print install suggestions for common Linux distributions.

In [15]:
# Automated Chromium/Chrome HTML -> PDF converter
from pathlib import Path
import shutil
import subprocess
import sys

nb = Path('data-visualization-demo.ipynb')
html = nb.with_suffix('.html')
out_pdf = nb.with_suffix('.chromium.pdf')

if not html.exists():
    print(f'HTML file not found at {html.resolve()}. Run the HTML export step first (nbconvert to html) or re-run the previous cell that exports HTML.')
else:
    # Common executable names for chromium/google-chrome
    candidates = ['chromium', 'chromium-browser', 'chromium-stable', 'google-chrome', 'google-chrome-stable', 'chrome']
    exe = None
    for c in candidates:
        path = shutil.which(c)
        if path:
            exe = path
            break

    if not exe:
        print('No Chromium/Chrome executable found on PATH.')
        print('Install suggestions (Debian/Ubuntu): sudo apt install chromium-browser')
        print('Or on Debian/Ubuntu to get a recent Chrome: install google-chrome-stable from Google repository.')
    else:
        # Use an absolute path to the HTML file
        html_path = str(html.resolve())
        pdf_path = str(out_pdf.resolve())
        cmd = [exe, '--headless', '--no-sandbox', '--disable-gpu', f'--print-to-pdf={pdf_path}', html_path]
        print('Running:', ' '.join(cmd))
        res = subprocess.run(cmd, stdout=subprocess.PIPE, stderr=subprocess.STDOUT, text=True)
        print(res.stdout)
        if res.returncode == 0 and Path(pdf_path).exists():
            print(f'Chromium produced PDF: {pdf_path}')
        else:
            print('Chromium print-to-pdf failed. Review the output above. As an alternative, open the HTML in a browser and use Print -> Save as PDF.')

No Chromium/Chrome executable found on PATH.
Install suggestions (Debian/Ubuntu): sudo apt install chromium-browser
Or on Debian/Ubuntu to get a recent Chrome: install google-chrome-stable from Google repository.